SpaceTimeLab

SpaceTimeLab’s mission is to generate actionable insights from geo-located and time-stamped data for government, business and society.

About

Working with the private and public sectors, SpaceTimeLab uses integrated space-time thinking and a multi-disciplinary approach to develop theories, algorithms and platforms to gain insight from geo-located and time-stamped data, in order to engineer solutions to improve the mobility, security, health and resilience of urban living.

Find out more about our research topics and data sources by looking at our leaflets:

Every year the UCL SpaceTimeLab is happy to host a small number of outstanding visiting researchers and doctoral students. We especially welcome those who closely share our research interests of big-data analytics and network complexity. If you are appointed, you will work closely with members of the SpaceTimeLab to develop an research plan which will be mutually beneficial. In order to make the most effective use of your time with us, we recommend that plans are made well before your visit, typically several months in advance.

Research

The transportation system is the lifeblood of the city. At SpaceTimeLab, we work to ensure the health of the city by improving the mobility of its citizens and the function of its transportation system. SpaceTimeLab is working on a number of projects (e.g. STANDARD) using large, spatio-temporal transportation datasets to categorise, cluster and profile people and places.

Examples of our work include:

Prediction, simulation and visualisation of urban traffic flows

Identifying flexible travellers and groups using smart card data

Modelling the impact of engineering work and incidents on London tube lines

The safety and security of the public relies on police agencies maximising their use of the diverse geo-temporal data sources available to them. Collaborating with the Metropolitan Police Service, the Crime, Policing and Citizenship (CPC) project analyses and models the relationship between police activities, crime and public attitudes towards policing. CPC focuses on:

Contribute towards ensuring the future sustainability of UK research using consumer data

Support consumer related organisations to maximise their innovation potential

Drive economic growth

We are bringing together world-class researchers from the University of Leeds, University College London, University of Liverpool and the University of Oxford to offer a range of expert services to a wide range of users.

The achievements of the European Union targets regarding energy and socio-economic sustainability are highly dependent on the way risks and vulnerabilities of European operating infrastructure networks and critical assets are minimised against natural extreme events. The INFRARISK project will develop reliable stress tests on European critical infrastructure using integrated modelling tools for decision-support. It will lead to higher infrastructure networks resilience to rare and low probability extreme events, known as “black swans”. INFRARISK will advance decision making approaches and lead to better protection of existing infrastructure while achieving more robust strategies for the development of new ones. INFRARISK proposes to expand existing stress test procedures and adapt them to critical land-based infrastructure which may be exposed to or threatened by natural hazards. Integrated risk mitigation scenarios and strategies will be employed, using local, national and pan-European infrastructure risk analysis methodologies. These will take into consideration multiple hazards and risks with cascading impact assessments.

The INFRARISK approach will robustly model spatio-temporal processes with propagated dynamic uncertainties in multiple risk complexity scenarios of Known Unknowns and Unknown Unknowns. An operational framework with cascading hazards, impacts and dependent geospatial vulnerabilities will be developed. This framework will be a central driver to practical software tools and guidelines that provide greater support to the next generation of European infrastructure managers to analyse and handle scenarios of extreme events. The minimisation of the impact of such events by the supporting tools shall establish optimum mitigation measures and rapid response. INFRASRISK will deliver a collaborative integrated platform where risk management professionals access and share data, information and risk scenarios results efficiently and intuitively.

The UCL Crime, Policing and Citizenship (CPC) project aims to investigate the relationship between detailed patterns of police activities and the space-time pattern of recorded incidents and public perceptions of crime. The project is being carried out in collaboration with the Metropolitan Police, with support from the UK Engineering and Physical Sciences Research Council (EPSRC).

UCL together with Transport for London (TfL) work on a 3-year EPSRC-funded research project to investigate spatio-temporal characteristics of data from transport networks. The Spatio-Temporal Analysis of Network Data and Route Dynamnics (STANDARD) project encompasses and builds upon a broad range of modelling approaches in spatio-temporal analysis, complexity science and state of the art 3D visualisation.

Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country. Modelling this kind of activity on the aggregate scale is very important for applications like measuring time expenditures and quality of life, tourist activity and environmental issues. Nowadays, attempts have been made to automatically infer transportation modes from positional data, such as the data collected by using GPS devices so that the cost in time and budget of conventional travel diary survey could be significantly reduced. Some limitations, however, exist in the literature, in aspects of data collection (sample size selected, duration of study, granularity of data), selection of variables (or combination of variables), and method of inference (the number of transportation modes to be used in the learning).

This project aims to fully understand these aspects in the process of inference. The work attempts to solve a classification problem of GPS data into different transportation modes (car, walk, cycle, underground, train and bus). We first study the variables that could contribute positively to this classification, and statistically quantify their discriminatory power. We then introduce a novel approach to carry out this inference using a framework based on Support Vector Machines (SVM) classification. We then apply segmentation strategies to identify significant stops that occurred along a person’s track such as home, work, etc. The classification then is subjected to a network matching process that checks whether the identified modes follow their corresponding transport networks to verify the final classification. This project is sponsored by u-blox and EPSRC.

Attesting to the powerful capabilities and in technology trends, many scholars envisioned the consolidation of Geographic Information Systems (GIS) into vital tools for disseminating spatial information, that are presently used to inform, advise and instruct users in several contexts and to further engage citizens in decision-making processes that can impact and sustain policy development. Interaction with these applications incorporates risk and uncertainty, which have been repeatedly identified as preconditions in nurturing trust perceptions, and which instigate a user’s decision to rely on a system and act on the provided information.

In a four-year project carried out in collaboration with Arup and with the support from the UK Engineering and Physical Sciences Research Council (EPSRC), Artemis Skarlatidou, used a multidisciplinary research approach derived mainly from the fields of Human-Computer Interaction and Risk Communication, to identify how non-experts' trust perceptions are formed when they interact with environmental Web GIS applications, but also how information about nuclear waste should be communicated to lay people to improve public understanding and trust. The findings supported the development of the PE-Nuclear tool; a Web GIS application to inform lay people in the UK about the site selection of a nuclear waste repository.

In a different project, also funded by the UK Engineering and Physical Sciences Research Council (EPSRC), we use the same approach to identify how non-experts' trust perceptions are formed when they interact with public crime Web GIS. One part of this research focuses on identifying the user needs and expectations when they interact with different types of crime data at different scales and for different purposes, while the other part aims at building novel crime visualisation approaches which are evaluated for their perceived trustworthiness with non-expert users.

Partnerships

Teaching

SpaceTimeLab welcomes applications from prospective PhD students from both the UK and overseas. Before applying, please look at our publications to ensure that your research interests match those of the lab. For more information on the requirements and application process, please check the CEGE postgraduate research page and the UCL Doctoral School page.

SpaceTimeLab also has strong links to the following taught MSc programmes:

Fisher, P., Wood, J., Cheng, T. (2004). Where is Helvellyn? Multiscale morphometry and the mountains of the English Lake District. Transactions of the Institute of British Geographers 29(1), 106-128 doi:10.1111/j.0020-2754.2004.00117.x.

Skarlatidou, A., Cheng, T., Haklay, M. (2012). What Do Lay People Want to Know About the Disposal of Nuclear Waste? A Mental Model Approach to the Design and Development of an Online Risk Communication. Risk Analysis 32(9), 1496-1511 doi:10.1111/j.1539-6924.2011.01773.x. Publisher URL

Skarlatidou, A., Haklay, M., CHENG, T. (2011). Trust in Web GIS: The role of the trustee attributes in the design of trustworthy Web GIS applications. International Journal of GIScience 25(12), 1913-1930 doi:10.1080/13658816.2011.557379.Publisher URL

Skarlatidou, A., Cheng, T., Haklay, M. (2012). What Do Lay People Want to Know About the Disposal of Nuclear Waste? A Mental Model Approach to the Design and Development of an Online Risk Communication. Risk Analysis 32(9), 1496-1511 doi:10.1111/j.1539-6924.2011.01773.x. Publisher URL

Fisher, P., Wood, J., Cheng, T. (2004). Where is Helvellyn? Multiscale morphometry and the mountains of the English Lake District. Transactions of the Institute of British Geographers 29(1), 106-128 doi:10.1111/j.0020-2754.2004.00117.x.

Anbaroglu, B., CHENG, T. (2011). Where and when does the traffic congestion begin and end? A spatio-temporal clustering approach to detect congestion. Proceedings of the international symposium on spatial-temporal analysis and data mining. ( pp.11-13). London, UK: University College London.

CHENG, T. (1998). A process-oriented spatio-temporal data model to support physical environmental modeling. the first conference of the Association of Geographic Information Laboratories in Europe (AGILE).

Cheng, T. (1998). Change of fuzzy objects. the first conference of Association of Geographic Information Laboratories in Europe (AGILE). Enschede, The Netherlands: